# OpenTelemetry Observability Mesh

URL: https://justrunme.com/cases/opentelemetry-observability-mesh/
Author: Andrey Lesnikov
Role: Senior Infrastructure & DevOps Engineer, CONTACT Software GmbH
Topics: AI infrastructure, Kubernetes, GitOps, Terraform, observability, platform engineering

An observability mesh connects traces, metrics, logs, runbooks, and ownership so production behavior can be understood from user request to Kubernetes workload.

## Snapshot

- Problem: Metrics, logs, and traces often exist separately, making incidents slower and ownership unclear.
- Constraints: Cardinality, sampling, cost, multi-service correlation, dashboard sprawl, and alert fatigue.
- Architecture: OpenTelemetry collection layer with service conventions, trace context propagation, metric normalization, log correlation, and dashboard/runbook links.
- Result: Production behavior becomes easier to understand from request path to workload to infrastructure signal.

## Architecture Notes

### Problem

Dashboards often show separate infrastructure metrics while incidents require a connected story: which request failed, which service owned it, which dependency slowed down, and which deployment changed recently.

### Architecture

OpenTelemetry collectors normalize service signals, propagate trace context, correlate logs with spans, connect metrics to SLOs, and link dashboards to runbooks. The mesh treats telemetry as platform design, not a sidecar afterthought.

### Trade-offs

High-cardinality telemetry and aggressive tracing can become expensive. Sampling, conventions, ownership tags, and budget-aware retention are part of the architecture.

### Result

Operators can move from symptom to owner faster. Teams see request paths, SLO burn, logs, and infrastructure context in one investigation flow.

## Architecture Diagram

Service request -> OTel SDK -> Collector -> Metrics/traces/logs -> SLO + runbook

## Constraints

- Connect traces, metrics, and logs without creating uncontrolled cardinality or storage cost.
- Make telemetry useful for ownership and incident response, not just dashboard aesthetics.
- Preserve enough context to move from user symptom to workload and dependency quickly.

## Key Decisions

- Use OpenTelemetry conventions and collectors as the platform telemetry boundary.
- Correlate traces, logs, service ownership, SLOs, and runbook links in the investigation path.
- Apply sampling and retention decisions as part of platform design, not after cost spikes.

## Failure Modes

- Dashboards show infrastructure health while user-facing requests are degraded.
- Trace data is too sparse for debugging or too noisy to afford.
- Alerts fire without ownership, context, or nearby recovery instructions.

## Result

Operators can move from symptom to owner faster with connected request paths, workload signals, SLO burn, and runbook context.

## Related Technologies

- OpenTelemetry
- Prometheus
- Grafana
- Loki
- Tempo
- SLOs
- Kubernetes
- Runbooks


## FAQ

### Why use OpenTelemetry for platform observability?

It standardizes traces, metrics, logs, and context propagation across services and vendors, making observability more portable and consistent.

### What is an observability mesh?

It is a connected telemetry layer where service traces, metrics, logs, ownership, SLOs, and runbooks reinforce each other during normal operations and incidents.

## Related URLs

- Case index: https://justrunme.com/cases/
- AI infrastructure hub: https://justrunme.com/ai-infrastructure/
- Kubernetes GitOps hub: https://justrunme.com/kubernetes-gitops/
- Profile: https://justrunme.com/